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CosmoPower is a library for Machine Learning - accelerated Bayesian inference. While the emphasis is on building algorithms to accelerate Bayesian inference in cosmology, the interdisciplinary nature of the methodologies implemented in the package allows for their application across a wide range of scientific fields. The ultimate goal of CosmoPower is to solve inverse problems in science, by developing Bayesian inference pipelines that leverage the computational power of Machine Learning to accelerate the inference process. This approach represents a principled application of Machine Learning to scientific research, with the Machine Learning component embedded within a rigorous framework for uncertainty quantification.

In cosmology, CosmoPower aims to become a fully differentiable library for cosmological analyses. Currently, CosmoPower provides neural network emulators of matter and Cosmic Microwave Background power spectra. These emulators can be used to replace Boltzmann codes such as CAMB or CLASS in cosmological inference pipelines, to source the power spectra needed for two-point statistics analyses. This provides orders-of-magnitude acceleration to the inference pipeline and integrates naturally with efficient techniques for sampling very high-dimensional parameter spaces. The power spectra emulators implemented in CosmoPower, and first presented in its release paper, have been applied to the analysis of real cosmological data from experiments, as well as having been tested against the accuracy requirements for the analysis of next-generation cosmological surveys.

CosmoPower is written entirely in Python. Neural networks are implemented using the TensorFlow library.